The present invention pertains to the medical diagnostic arts. It finds particular application in conjunction with identifying and quantizing the ventricular volumes in a patient's heart from magnetic resonance image data and will be described with particular reference thereto. It is to be appreciated, however, that the invention may also find application in conjunction with identifying other regions of human or non-human subjects, determining a volume of identified regions, and the like, with either magnetic resonance or other imaging modalities.
Heretofore, ventricular volumes have been determined by first generating a volumetric image representation of a rectangular portion of the patient's body that includes the heart. Commonly, the volume image representation included a plurality of parallel planar or slice images which were coordinated with the cardiac cycle such that each slice or plane was taken at or near the same cardiac phase. A trained radiologist or technician viewed each slice and marked the edges of the ventricles. Once the boundary of the ventricle regions was marked, the volume could be readily calculated. For example, the number of voxels in the volume within the boundary or the number of voxels of each slice within the boundary could be counted. From the dimensions of the voxel or the dimensions of the voxels and the interslice spacing, the volume was readily calculated. Not only did this technique require a large amount of time, but the results were not reproduceable. Each radiologist or other trained expert tended to define the edges of the ventricles differently.
Various automatic methods have been proposed for determining the ventricular volumes without human assistance. Because the blood and the tissue have different gray scale or intensity, one could use this difference to determine the boundary. Boundaries or edges of the magnetic resonance image are characterized by high frequency components. Noise, motion, and other artifacts are present at all frequencies but most significant in the high frequency components. This noise created uncertainty in the location of the interface. In one approach, a contour following algorithm was applied to the image to make a first approximation of the boundary of the ventricle in each slice. Density profiles on either side of the proposed border were examined and the contour following algorithm was reapplied. This procedure was repeated iteratively until a stable threshold was obtained. This iterative approach was not only time consuming and computationally expensive, but tended to have inaccuracies arising from the inclusion of surrounding tissue other than ventricles in the process.
Another drawback to this method is that it assumed that the intensity profile passing through the heart/blood interface was sigmoidal, with the optimal edge position existing at voxels having the largest gradient. This assumption is unsupported. When the intensity profile is not sigmoidal, there are errors in the estimate of the cardiac volume.
In a second technique, a threshold was used to create a binary image from the volume image. That is, the intensity or gray scale of each voxel was examined and compared with a threshold. Based on this comparison, each pixel or voxel was classified as blood, hence an interior region of the ventricle, or non-blood. Identifying the threshold value commonly required an initial human guess. The guess was iteratively adjusted in repetitions of the binary image forming process until appropriate results were achieved. This technique was again slow and computationally burdensome.
Another technique used a priori probabilities for the expected tissue classes within the volume to produce an initial segmentation of the image, i.e. define the ventricle boundaries. These probabilities were updated in accordance with the initial image and the segmentation process repeated with the updated probabilities. This process was iteratively repeated until the segmentation reached a steady state. This technique required some manual segmentation to create a training set and to determine the initial probabilities. Moreover, modifying the decision criteria, i.e. the probabilities, based on results tended to perpetuate inaccuracies.
In another approach, each voxel was classified based on its gray scale level, gradient, and location. The voxel classifications were iteratively updated in order to make the relative classifications more consistent using the Dempster-Schafer theory of knowledge. This technique again required a priori information and was computationally excessive. Moreover, because the iterations were based on prior iterations, errors were compounded.
The present invention contemplates a new and improved voluming technique which overcomes the above-referenced problems and others.